冷门人才求职绝招

以下是小编为大家准备的冷门人才求职绝招，本文共6篇，仅供参考，大家一起来看看吧。本文原稿由网友“Simple头”提供。

篇1：冷门人才 求职绝招

与谁换位思考

与招聘单位进行换位思考，

冷门人才 求职绝招

与竞聘对象进行换位思考。揣度他们最想到哪些单位就业，此后，再进行特定单位招聘人才供需情况的调查，并从自己专业较冷的实际出发，适当进行“人弃我取”，到那些招聘人才供不应求的单位求职；

“冷择业”守则

从“冷单位”中寻找成长性企业，将那些虽然目前形势不佳，但已经在进行技术改造、技术创新、通过资产重组后有发展前景的新项目，并且显示出较好成长性的“冷单位”，作为自己的优选对象；

从“冷专业”中寻找朝阳性行业。一些单位招聘的专业岗位较冷，绝大多数求职者不愿“冷就”，这些专业虽然目前不太吃香，但以发展的眼光来看，却像朝阳一样，在未来会如日中天；

从“冷行业”中寻找发展性亮点。由于交通、资源开发和政策等方面的因素的变化，这些“冷行业”峰回路转，已经闪出发展亮点，将来大有可为已成大势所趋，到这些“冷行业”就业，也是一种理智的抉择。

篇2：冷门人才求职绝招

与谁换位思考

与招聘单位进行换位思考。看看他们最想招聘的是哪些人，在专业结构、年龄结构、知识层次结构等方面有什么具体要求，然后拿自己与之对照，如竞争者众多，且条件又超过自己，则及早理智地退出，另寻他就；

与竞聘对象进行换位思考。揣度他们最想到哪些单位就业，此后，再进行特定单位招聘人才供需情况的调查，并从自己专业较冷的实际出发，适当进行“人弃我取”，到那些招聘人才供不应求的单位求职；

“冷择业”守则

从“冷单位”中寻找成长性企业。将那些虽然目前形势不佳，但已经在进行技术改造、技术创新、通过资产重组后有发展前景的新项目，并且显示出较好成长性的“冷单位”，作为自己的优选对象；

从“冷专业”中寻找朝阳性行业。一些单位招聘的专业岗位较冷，绝大多数求职者不愿“冷就”，这些专业虽然目前不太吃香，但以发展的眼光来看，却像朝阳一样，在未来会如日中天；

从“冷行业”中寻找发展性亮点。由于交通、资源开发和政策等方面的因素的变化，这些“冷行业”峰回路转，已经闪出发展亮点，将来大有可为已成大势所趋，到这些“冷行业”就业，也是一种理智的抉择。.

Intelligent Inference Systems Corp., Sunnyvale, CA Research Scientist

April 2002 – April 2003: Started a new research initiative in applying the ACFRL algorithm and the previously developed multi-agent coordination algorithms to power control in wireless networks. Published several conference papers on this topic. Results demonstrate an improvement by more than a factor of 2 in comparison with the algorithms used in IS-95 and CDMA2000 standards.

April 2002 – April 2003: Wrote a Phase I STTR proposal to the Office of Naval Research and received funding for the topic of “Perception-based co-evolutionary reinforcement learning for UAV sensor allocation.” Developed theoretical algorithms and designed a practical implementation strategy, which demonstrated excellent results in a high-fidelity robotic simulator. Published a conference paper.

October 1998 – April 2002: Wrote a proposal to the NASA Program in Thinking Systems and received multi-year funding for the topic of cooperation and coordination in multi-agent systems. Developed, evaluated, and published new Reinforcement Learning algorithms for dynamic resource allocation among distributed agents operating jointly in complex, uncertain, and nonstationary environments.

Fall 2000: Developed a new algorithm for single-agent learning in noisy dynamic environments with delayed rewards: Actor-Critic Fuzzy Reinforcement Learning (ACFRL). Published a conference and a journal paper with a convergence proof for ACFRL. US patent (number 6,917,925) was granted for the ACFRL algorithm on July 12, 2005.

ChainCast Inc., San Jose, CA

Aug 2000 – Oct 2000:  Conducted a survey of techniques for dynamic updating of multicasting trees and suggested a novel approach based on using multi-agent learning.

NASA Ames Research Center, Moffet Field, CA Summer 1998:  Designed a framework for multiple agents operating in a complex, uncertain, and nonstationary environment. Agents learn to improve their policies using fuzzy reinforcement learning.

SRI International, Artificial Intelligence Center, Menlo Park, CA

Summer 1998: Developed a methodology for representing a replanning problem in the space of plans as a reinforcement learning problem.

Bear, Stearns & Co., Inc. - Proprietory Trading Department, New York, NY

Summer 1996, 1997:  Conducted a comprehensive study of time series forecasting models with neural networks. Recommended a hybrid model combining best features of the existing models and implemented it in C++.

Summer 1995:  Developed a stock forecasting system based on conventional econometric techniques and implemented it in SAS language. Gained exposure to various proprietary trading models.

Alphatech, Inc., Burlington, MA

Feb 1997 - May 1997:  Developed an algorithm for optimal control of macroeconomic systems described by simultaneous-time equations and implemented it in MATLAB.

Arthur Andersen, Inc., Boston, MA

Feb 1996 - May 1996:  Developed an internal System Dynamics cashflow model of startup businesses. Gained experience in management level client interactions and in project presentation skills.

Summer 1996:  Independently designed a game theoretic bid forecasting system in procurement auctions for a large construction company. The project involved extensive on-site client interactions during model development as well as a final presentation to the top level management.

Property & Portfolio Research, Inc., Boston, MA

Feb 1994 - May 1995:  Designed a mortgage portfolio analysis model and implemented it in Visual Basic for Excel. Developed a methodology for grouping real estate time series using cluster and factor analyses in SPSS. Designed an optimal investment strategy for a class of mortgage backed securities based on the efficient frontier characteristics. Gained broad exposure to real estate markets and models.

Donaldson, Lufkin & Jenrette, Inc. -- Pershing Division, Jersey City, NJ

Summer 1994:  Developed a stock forecasting system based on technical analysis and economic indicators. Developed a DJIA trading strategy based on S&P 500 futures and demonstrated its profitability.

MIT Laboratory for Information and Decision Systems, Cambridge, MA

Aug 1993 - May 1994:  Developed a trading strategy for US Treasury bonds based on multi-resolution wavelet analysis. Demonstrated its profitability as compared to the conventional moving average models.

PROGRAMMING

C++, Java, MATLAB; Various packages for statistics, neural networks and system dynamics.

PUBLICATIONS

Published 13 papers in refereed conferences, 8 journal papers, 1 book chapter. The complete list, including technical reports, is available at research.sun.com/people/vengerov/publications.html.

PATENTS

Four patents granted, 10 patent applications are currently under review at the US Patent Bureau.

PERSONAL

United States Citizen. Fluent in Russian and English. Black belt and instructor in Tae Kwon Do.

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绝招范文

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